An Improved Real-Coded Population-Based Extremal Optimization Method for Continuous Unconstrained Optimization Problems

Table 4

Parameters used in different evolutionary algorithms.

Algorithm	Number of parameters	Parameters
GA [24]	At least 4	Population size (SP), maximum number of iterations (𝐼max), the probability of crossover, the parameter used in mutation operations
PSO [22]	At least 6	SP, 𝐼max, inertia weight factor 𝑤max, 𝑤min, acceleration parameter 𝑐1, 𝑐2
PEO [22]	At least 4	SP, 𝐼max, dynamical multi-𝛼 (i.e., parameter used in Lévy mutation)
PSO-EO [22]	8	SP, 𝐼max, 𝑤max, 𝑤min, 𝑐1, 𝑐2, TC and TG used in the hybrid GC mutation
IRPEO	3	SP, 𝐼max, power-law coefficient 𝜏